<i>HFE</i>Mutations in Caucasian Participants of the Hemochromatosis and Iron Overload Screening Study with Serum Ferritin Level <1000 μg/L
Bibliographic record
Abstract
BACKGROUND: Many patients referred for an elevated serum ferritin level <1000 µg⁄L are advised that they likely have iron overload and hemochromatosis. AIMS: To determine the prevalence of HFE mutations in the hemochromatosis gene for 11 serum ferritin concentration intervals from 200 µg⁄L to 1000 µg⁄L in Caucasian participants in a primary care, population-based study. METHODS: The Hemochromatosis and Iron Overload Screening study screened 99,711 participants for serum ferritin levels, transferrin saturation and genetic testing for the C282Y and H63D mutations of the HFE gene. This analysis was confined to 17,160 male and 27,465 female Caucasian participants because the HFE C282Y mutation is rare in other races. Post-test likelihood was calculated for prediction of C282Y homozygosity from a ferritin interval. A subgroup analysis was performed in participants with both an elevated serum ferritin level and transferrin saturation. RESULTS: There were 3359 male and 2416 female participants with an elevated serum ferritin level (200 µg⁄L to 1000 µg⁄L for women, 300 µg⁄L to 1000 µg⁄L for men). There were 69 male (2.1%) and 87 female (3.6%) C282Y homozygotes, and the probability of being a homozygote increased as the ferritin level increased. Post-test likelihood values were 0.3% to 16% in men and 0.3% to 30.4% in women. CONCLUSIONS: Iron loading HFE mutations are unlikely to be the most common cause of an elevated serum ferritin level in patients with mild hyperferritinemia. Patients should be advised that there are many causes of an elevated serum ferritin level including iron overload.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".